Data processing systems with machine learning engines for dynamically generating risk index dashboards

ABSTRACT

Methods, computer-readable media, software, and apparatuses include receiving, from a plurality of risk information sources, risk information associated with a user account, wherein the risk information includes a plurality of risk components, determining, for each of the plurality of risk components, an impact score and a risk probability by applying a machine learning model to risk information associated with the user account, generating an interactive risk index dashboard including a plurality of interactive risk index elements, wherein each of the plurality of interactive risk index elements is associated with a risk component of the plurality of risk components, and displaying, on the display of the apparatus, the interactive risk index dashboard, wherein each of the plurality of interactive risk index elements is displayed in a portion of the interactive risk index dashboard in accordance with a respective determined impact score and risk probability.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of and claims priority to U.S.patent application Ser. No. 17/179,790 filed on Feb. 19, 2021, which isincorporated by reference in its entirety herein.

FIELD OF ART

Aspects of the disclosure generally relate to methods and computersystems, including one or more computers particularly configured and/orexecuting computer software and one or more sensors or devices operablyconnected to the one or more computers. More specifically, aspects ofthis disclosure relate to systems and methods for determining one ormore risk indices associated with a user account and displaying the riskindices in an interactive risk index dashboard on one or more displaydevices.

BACKGROUND

Insurance policies are generally purchased by customers from variousinsurance providers. Conventional policies generally provide coverage tothe user for a term of the policy based on payment of a premiumassociated with the policy. Such term based policies might not accountfor factors such as customer behaviors, environmental conditions,biometric information, or the like. Rather, coverage may be provided forthe term, regardless of such factors.

Many devices include sensors and internal computer systems designed tomonitor, store and transmit various types of data, such as biometricdata, health data, vehicle data, environmental conditions, digitalinformation, property condition data, and the like. Many devices alsoinclude one or more communication systems designed to send and receiveinformation from inside or outside the device. Such information caninclude various types of additional, related data.

Despite advances in various technologies, however, it may be difficultto effectively identify and evaluate various risks associated with auser and to present such risks to a user in an easy to understand andinteractive format. Such effective identification and presentation maybe hampered by lack of tools to cohesively collect a wide range ofinformation from many different sources.

SUMMARY

In light of the foregoing background, the following presents asimplified summary of the present disclosure in order to provide a basicunderstanding of some aspects of the invention. This summary is not anextensive overview of the invention. It is not intended to identify keyor critical elements of the invention or to delineate the scope of theinvention. The following summary merely presents some concepts of theinvention in a simplified form as a prelude to the more detaileddescription provided below.

Aspects of the disclosure address one or more of the issues mentionedabove by disclosing methods, computer readable storage media, software,systems, and apparatuses for determining one or more risk indicesassociated with a user and displaying the risk indices in an interactivedashboard on one or more display devices. In particular, based oncollected information from a plurality of sources associated with a useraccount, one or more risk indices may be determined. Accordingly, basedon the collected information, one or more risk indices may beidentified, calculated, and/or determined.

Advantageous solutions to the problems presented above, and other issueswhich will be apparent upon the reading of the present disclosure, maybe to provide an apparatus that includes one or more processors, andmemory storing instructions that, when executed by the one or moreprocessors, cause the apparatus to receive, from a plurality of riskinformation sources, risk information associated with a user account,wherein the risk information includes a plurality of risk components,determine, for each of the plurality of risk components, an impact scoreand a risk probability by applying a machine learning model to riskinformation associated with the user account, generate an interactiverisk index dashboard including a plurality of interactive risk indexelements, wherein each of the plurality of interactive risk indexelements is associated with a risk component of the plurality of riskcomponents, and display, on the display of the apparatus, theinteractive risk index dashboard, wherein each of the plurality ofinteractive risk index elements is displayed in a portion of theinteractive risk index dashboard in accordance with a respectivedetermined impact score and risk probability.

In some aspects, the memory may store additional computer-readableinstructions that, when executed by the one or more processors, furthercause the apparatus to receive a user interaction with a portion of theinteractive risk index dashboard, and, display additional informationassociated with the portion of the interactive risk index dashboardbased on the user interaction.

In some aspects, the plurality of interactive risk index elements may bedisplayed in a grid format in the interactive risk index dashboard,wherein a first axis in the grid format displays the impact score and asecond axis in the grid format displays the risk probability.

In some aspects, each of the plurality of interactive risk indexelements may include one or more components of additional information,and the one or more components of additional information may bedisplayed with a respective interactive risk index element or may beprovided in a sub-display of the interactive risk index dashboard upon auser interaction with a respective interactive risk index element. Theone or more components of additional information include at least oneof: a descriptor, a categorical indicator, a risk coding indicator, or arisk reduction recommendation.

In some aspects, the memory may store additional computer-readableinstructions that, when executed by the one or more processors, furthercause the apparatus to determine a total risk index based on theplurality of risk components, and display, in a portion of theinteractive risk index dashboard, the total risk index.

In some examples, the memory may store additional computer-readableinstructions that, when executed by the one or more processors, furthercause the apparatus to display a user prompt requesting additional riskinformation associated with the user account, receive user inputproviding the requested additional risk information, and update at leastone of the plurality of interactive risk index elements in theinteractive risk index dashboard in accordance with the received userinput.

In some arrangements, the memory may store additional computer-readableinstructions that, when executed by the one or more processors, furthercause the apparatus to determine a confidence level for each of theplurality of risk components, wherein the confidence level relates to areliability of an associated risk information source.

In some aspects, the memory may store additional computer-readableinstructions that, when executed by the one or more processors, furthercause the apparatus to calculate a projected risk probability of afuture risk occurrence based on at least one of the plurality of riskcomponents.

In some example arrangements, the memory may store additionalcomputer-readable instructions that, when executed by the one or moreprocessors, further cause the apparatus to generate a first offer for arisk index-based insurance policy based on at least one of the pluralityof risk components and current information associated with the at leastone of the plurality of risk components, and generate a second offer fora risk index-based insurance policy based on the at least one of theplurality of risk components and based on the user completing a riskreduction recommendation associated with the at least one of theplurality of risk components, wherein the first offer, the second offer,and the risk reduction recommendation are displayed with a respectiveinteractive risk index element or are provided in a sub-display of theinteractive risk index dashboard upon a user interaction with arespective interactive risk index element.

In accordance with further aspects of the present disclosure, a methoddisclosed herein may include receiving, from a plurality of riskinformation sources, risk information associated with a user account,wherein the risk information includes a plurality of risk components,determining, for each of the plurality of risk components, a risk score,the risk score including an impact score and a risk probability byapplying a machine learning model to risk information associated withthe user account, generating an interactive risk index dashboardincluding a plurality of interactive risk index elements, wherein eachof the plurality of interactive risk index elements is associated with arisk component of the plurality of risk components, displaying, on adisplay of a user device, the interactive risk index dashboard, whereineach of the plurality of interactive risk index elements is displayed ina portion of the interactive risk index dashboard in accordance with arespective determined impact score and risk probability, and responsiveto receiving a user interaction at an interactive risk index element inthe interactive risk index dashboard, providing an additionalsub-display with information relating to the interactive risk indexelement.

In some aspects, the method may further include receiving a userinteraction with a portion of the interactive risk index dashboard, andbased on the user interaction, displaying one or more components ofadditional information associated with the portion of the interactiverisk index dashboard.

In some aspects, displaying the interactive risk index dashboard mayinclude displaying the plurality of interactive risk index elements in agrid format in the interactive risk index dashboard, wherein a firstaxis in the grid format displays the impact score and a second axis inthe grid format displays the risk probability.

In some examples, each of the plurality of interactive risk indexelements may include one or more components of additional information,and the method may further include displaying one or more components ofadditional information with a respective interactive risk index elementor in a sub-display of the interactive risk index dashboard uponreceiving a user interaction with a respective interactive risk indexelement. In some examples the one or more components of additionalinformation may include at least one of: a descriptor, a categoricalindicator, a risk coding indicator, or a risk reduction recommendation.

In accordance with further aspects of the present disclosure, a systemdisclosed herein may include a first computing device and a secondcomputing device in signal communication with the first computingdevice. The first computing device may include a display, a processor,and memory storing instructions that, when executed by the processor,cause the first computing device to: receive, from the at least oneother computing device, risk information associated with a user account,wherein the risk information includes a plurality of risk components,determine, for each of the plurality of risk components, an impact scoreand a risk probability by applying a machine learning model to riskinformation associated with the user account, generate an interactiverisk index dashboard including a plurality of interactive risk indexelements, wherein each of the plurality of interactive risk indexelements is associated with a risk component of the plurality of riskcomponents, and provide, on the display, the interactive risk indexdashboard, wherein each of the plurality of interactive risk indexelements is displayed in a portion of the interactive risk indexdashboard in accordance with a respective determined impact score andrisk probability.

In some arrangements, the system may further include a second computingdevice in signal communication with, wherein the second computing devicemay include a processor, at least one sensor, a wireless communicationinterface, and memory storing instructions that, when executed by theprocessor, cause the second computing device to: record sensor datausing the at least one sensor, wherein the sensor data is associatedwith a risk component of the plurality of risk components, and transmitthe sensor data to the first computing device. In some examples, the atleast one sensor may include a biometric device and the sensor data mayinclude biometric data relating to a user associated with the useraccount. In some examples, the at least one sensor may include atelematics device and the sensor data may include vehicle data relatingto a vehicle associated with the user account.

In some examples, each of the plurality of interactive risk indexelements may include one or more components of additional information,and the one or more components of additional information may bedisplayed with a respective interactive risk index element or may beprovided in a sub-display of the interactive risk index dashboard upon auser interaction with a respective interactive risk index element.

Methods and systems of the above-referenced embodiments may also includeother additional elements, steps, computer-executable instructions, orcomputer-readable data structures. In this regard, other embodiments aredisclosed and claimed herein as well. The details of these and otherembodiments of the present invention are set forth in the accompanyingdrawings and the description below. Other features and advantages of theinvention will be apparent from the description, drawings, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and is notlimited by the accompanying figures in which like reference numeralsindicate similar elements and in which:

FIG. 1 illustrates an example computing environment that may be used inaccordance with one or more aspects described herein.

FIG. 2 illustrates a block diagram illustrating the system architecturein accordance with one or more aspects described herein.

FIG. 3 illustrates a block diagram of a risk index determination andanalysis system in accordance with one or more aspects described herein.

FIG. 4 illustrates an exemplary flowchart for generating and displayingan interactive risk index dashboard in accordance with one or moreaspects described herein.

FIG. 5 illustrates an exemplary flowchart for generating a “what-if”scenario risk estimation of one or more forecasted events in accordancewith one or more aspects described herein.

FIGS. 6-8 illustrate exemplary interactive risk index dashboard userinterfaces in accordance with one or more aspects described herein.

DETAILED DESCRIPTION

In accordance with various aspects of the disclosure, methods,computer-readable media, software, and apparatuses are disclosed forgenerating and displaying an interactive risk index dashboard based onobtained information for a plurality of risk information sourcesassociated with a user account. As described herein, interactive riskindex dashboards may include a plurality of interactive risk elements,each associated with a respective different risk component.

In some instances, users may need to access a number of variousdifferent sources to understand a variety of risks that affect them. Asa result, it may be difficult for users to understand a succinct summaryof such risks. Also, for example, risk information, such as aprobability of a risk occurring or the impact of the occurrence on theuser, may be difficult to ascertain from information provided by variousorganizations. As another example, user may lack an ability to comparecertain types of risks relative to other different types of risks.Additionally, users are often unaware of potential mitigating actions orsteps that mat reduce the likelihood or negative impact associated withsuch risks.

In the following description of the various embodiments of thedisclosure, reference is made to the accompanying drawings, which form apart hereof, and in which is shown by way of illustration, variousembodiments in which the disclosure may be practiced. It is to beunderstood that other embodiments may be utilized and structural andfunctional modifications may be made.

As will be appreciated by one of skill in the art upon reading thefollowing disclosure, various aspects described herein may be embodiedas a method, a specially-programmed computer system, or a computerprogram product. Accordingly, those aspects may take the form of anentirely hardware embodiment, an entirely software embodiment or anembodiment combining software and hardware aspects. Furthermore, suchaspects may take the form of a computer program product stored by one ormore computer-readable storage media having computer-readable programcode, or instructions, embodied in or on the storage media. Any suitablecomputer readable storage media may be utilized, including hard disks,CD-ROMs, optical storage devices, magnetic storage devices, and/or anycombination thereof.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). In general, the one or morecomputer-readable media may be and/or include one or more non-transitorycomputer-readable media.

In one or more arrangements, aspects of the present disclosure may beimplemented with a computing device. FIG. 1 illustrates a block diagramof an example computing environment including risk index determinationdevice 100 (also referred to herein as a risk index analysis device or arisk index determination and analysis device) that may be used inaccordance with one or more aspects described herein. The risk indexdetermination device 100 may be a computing device, such as a personalcomputer (e.g., a desktop computer), server, laptop computer, notebook,tablet, smartphone, etc. The risk index determination device 100 mayhave a data collection module 101 for retrieving and/or analyzing dataas described herein. The data collection module 101 may be implementedwith one or more processors and one or more storage units (e.g.,databases, RAM, ROM, and other computer-readable media), one or moreapplication specific integrated circuits (ASICs), field programmablegate arrays (FPGA), and/or other hardware components (e.g., resistors,capacitors, power sources, switches, multiplexers, transistors,inverters, etc.). Throughout this disclosure, the data collection module101 may refer to the software and/or hardware used to implement the datacollection module 101. In cases where the data collection module 101includes one or more processors, such processors may be speciallyconfigured to perform the processes disclosed herein. Additionally, oralternatively, the data collection module 101 may include one or moreprocessors configured to execute computer-executable instructions, whichmay be stored on a storage medium, to perform the processes disclosedherein. In some examples, risk index determination device 100 mayinclude one or more processors 103 in addition to, or instead of, thedata collection module 101. The processor(s) 103 may be configured tooperate in conjunction with data collection module 101. Both the datacollection module 101 and the processor(s) 103 may be capable ofcontrolling operations of the risk index determination device 100 andits associated components, including RAM 105, ROM 107, an input/output(I/O) module 109, a network interface 111, and memory 113. For example,the data collection module 101 and processor(s) 103 may each beconfigured to read/write computer-executable instructions and othervalues from/to the RAM 105, ROM 107, and memory 113.

The I/O module 109 may be configured to be connected to an input device115, such as a microphone, keypad, keyboard, touchscreen, and/or stylusthrough which a user of the risk index determination device 100 mayprovide input data. The I/O module 109 may also be configured to beconnected to a display device 117, such as a monitor, television,touchscreen, etc., and may include a graphics card. For example, the I/Omodule 109 may be configured to receive biometric data from a user. Thedisplay device 117 and input device 115 are shown as separate elementsfrom the risk index determination device 100; however, they may bewithin the same structure. On some risk index determination devices 100,the input device 115 may be operated by a user or customer to interactwith the data collection module 101, including providing informationabout customer preferences, customer information, account information,etc., as described in further detail below. System administrators mayuse the input device 115 to make updates to the data collection module101, such as software updates. Meanwhile, the display device 117 mayassist the system administrators and users to confirm/appreciate theirinputs.

The memory 113 may be any computer-readable medium for storingcomputer-executable instructions (e.g., software). The instructionsstored within memory 113 may enable the risk index determination device100 to perform various functions. For example, memory 113 may storesoftware used by the risk index determination device 100, such as anoperating system 119 and application programs 121, and may include anassociated database 123.

Although not shown in FIG. 1 , various elements within memory 113 orother components in the risk index determination device 100, may includeone or more caches, for example, CPU caches used by the processing unit103, page caches used by the operating system 119, disk caches of a harddrive, and/or database caches used to cache content from database 123.For embodiments including a CPU cache, the CPU cache may be used by oneor more processors in the processor 103 to reduce memory latency andaccess time. In such examples, the processor 103 may retrieve data fromor write data to the CPU cache rather than reading/writing to memory113, which may improve the speed of these operations. In some examples,a database cache may be created in which certain data from a centraldatabase such as, for example, one or more enterprise servers 170 (e.g.,a claims database, an underwriting database, insurance customerdatabase, local information database, etc.) is cached in a separatesmaller database on an application server separate from the databaseserver. For instance, in a multi-tiered application, a database cache onan application server can reduce data retrieval and data manipulationtime by not needing to communicate over a network with a back-enddatabase server such as, for example, one or more enterprise servers170. These types of caches and others may be included in variousembodiments, and may provide potential advantages in certainimplementations of retrieving and analyzing driving data, such as fasterresponse times and less dependence on network conditions whentransmitting/receiving driving data from a vehicle 140 (e.g., fromvehicle-based devices such as on-board vehicle computers, short-rangevehicle communication systems, telematics devices), data from one ormore enterprise servers 170, etc.

The network interface 111 may allow risk index determination device 100to connect to and communicate with a network 130. The network 130 may beany type of network, including a local area network (LAN) and/or a widearea network (WAN), such as the Internet, a cellular network, orsatellite network. Through network 130, risk index determination device100 may communicate with one or more other computing devices such as auser device 150 or a user device 160 (e.g., laptops, notebooks,smartphones, tablets, personal computers, servers, vehicles, homemanagement devices, home security devices, smart appliances, etc.)associated with user or a user account. Through network 130, risk indexdetermination device 100 may also communicate with one or moreenterprise servers 170 to exchange related information and data.

Network interface 111 may connect to the network 130 via communicationlines, such as coaxial cable, fiber optic cable, etc., or wirelesslyusing a cellular backhaul or a wireless standard, such as IEEE 802.11,IEEE 802.15, IEEE 802.16, etc. Further, network interface 111 may usevarious protocols, including TCP/IP, Ethernet, File Transfer Protocol(FTP), Hypertext Transfer Protocol (HTTP), etc., to communicate withuser device 150, user device 160, and enterprise servers 170.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as Transmission Control Protocol/InternetProtocol (“TCP/IP”), Ethernet, File Transfer Protocol (“FTP”), HypertextTransfer Protocol (“HTTP”) and the like, and of various wirelesscommunication technologies such as the Global System for MobileCommunications (“GSM”), Code Division Multiple Access (“CDMA”), Wi-Fi,Long-Term Evolution (“LTE”), and Worldwide Interoperability forMicrowave Access (“WiMAX”), is presumed, and the various computingdevices and mobile device location and configuration system componentsdescribed herein may be configured to communicate using any of thesenetwork protocols or technologies.

FIG. 2 illustrates a schematic diagram of a risk index based insurancesystem 200 in accordance with one or more aspects described herein. Therisk index based insurance system 200 may be associated with, internalto, operated by, or the like, an entity 201, such as an insuranceprovider. In some examples, the entity may be one of various other typesof entities, such as a government entity, corporation or business,university, or the like. Various examples described herein will bediscussed in the context of an insurance provider. However, nothing inthe specification should be viewed as limiting use of the systems,methods, arrangements, etc. described herein to use only by an insuranceprovider.

The risk index based insurance system 200 may include one or moremodules that may include hardware and/or software configured to performvarious functions within the risk index based insurance system 200. Theone or more modules may be separate, physical devices or, in otherexamples, one or more modules may be part of the same physical device.

The risk index based insurance system 200 may include a risk indexmodule 202. The risk index module 202 may be configured to determine arate or a cost to insure an average user for a predetermined period oftime. For instance, the risk index module 202 may receive data, such asinsurance data from insurance data store 204, locality data fromlocality data store 206, as well as other data (from data stores notshown that may be internal to the entity 201 or external to the entity201), and determine, based on the received data, the cost to insure anaverage user over a predetermined period of time (e.g., one month, oneweek, one day, one year, or the like). Further, a cost to the user orinsurance policy holder to purchase an insurance policy may bedetermined by the system. This cost may be based, at least in part, onone or more risk indices and may be determined on a fixed date. The costto the user may then be revised at a second date (e.g., monthly,annually, etc.), e.g., based on an updated one or more risk indices.Accordingly, insurance may be provided to one or more users based onrisk indices, as will be discussed more fully herein.

The risk index based insurance system 200 may further include a policymodule 208. The policy module 208 may generate and/or store insurancepolicies using risk indices, as well as insurance policy information orfactors, such as vehicle, home, or other property information, drivingrecord/experience, policy limits, deductibles, etc. That is, a user maybe insured through a policy that assigns a number of risk indices fordetermining a particular cost (e.g., insurance premium). The risk indexor indices may continuously be updated (e.g., in real-time or nearreal-time) with additional information, for example, as the user drivesor operates his or her vehicle. The risk index or indices may beconsumed based on sensor data-focused factors. For example, in thecontext of vehicle insurance, such factors may include driving habits ofthe user, driving patterns, environmental conditions in which the userresides or operates his vehicle, vehicle parameters (such as year, make,model, features, specifications, etc.), condition or performance of thevehicle (e.g., based on sensor data), and the like, as well astraditional policy factors, such as driving experience, driving record,credit variables, policy coverage, deductible, policy limits,familiarity of the driver with the vehicle or surroundings, and thelike. In some examples, one policy parameter may include a level ofcoverage. For instance, insurance coverage may be purchased at variouslevels with each level providing a different level of coverage inaccordance with a determined risk index, as will be discussed more fullyherein. Additionally or alternatively, the rate may reflect differentlevels of coverage.

The insurance policies may further be based on a risk index accountstored in risk index update module 214. The risk index update module 214may include one or more accounts associated with one or more users(e.g., users having risk index based insurance policies), vehicles(e.g., vehicles associated with a risk index based insurance policies),homes (e.g., vehicles associated with a risk index based insurancepolicies), or the like. The accounts may include information associatedwith a user such as name, address, contact information, and the like, aswell as information associated with the vehicle, such as vehicleidentification number, make, model, year, etc., or informationassociated with the home, such as address, number of rooms, squarefootage, size of the land plot, etc. Further, the accounts may include anumber of risk indices associated with the user or account holder,associated with the vehicle or with the home, and the like. Accordingly,if a user has a risk index based insurance policy that includes acertain number of risk indices, the user account will show those riskindices. As additional information is received (e.g., by user updates,by receiving data from related devices, or the like) the various riskindices in the account may be updated. In some examples, the currentvalue of risk indices in an account may be displayed to the user via acomputing device, such as one or more of computing devices 212 a-212 fFor instance, a risk index value may be displayed via an application(e.g., online or mobile application) on a smartphone 212 a, personaldigital assistant (PDA) 212 b, tablet 212 c, a laptop 212 d, or othercomputing device 212 e. In some examples, a current risk index value maybe displayed to a user on a vehicle display 212 f In addition to displayof the risk infix factors, various other account details, and/or otherrelated information may be displayed as desired.

In some arrangements, the risk index account may include other types ofrisk data (e.g., other than risk indices). For instance, the risk indexaccount may include risk impact or probability. The related risk datamay be updated based on receipt of additional information, as discussedabove. In some examples, the risk indices may include one or morerecommendations for the user to reduce the risk indices or other riskdata associated with the risk indices that may be determined by applyinga machine learning model to risk information associated with the useraccount. In some examples, additional information provided with a riskindex may include a projected change in insurance premium upon a stepbeing taken (or not taken) by a user that may similarly be determined byapplying a machine learning model. Although various arrangementsdiscussed herein will be described in terms of risk indices and otherrisk data associated with the risk indices, various other metrics orfactors may be used (e.g., monetary units of risk) without departingfrom the present disclosure.

The risk index update module 214 may further include hardware and/orsoftware configured to determine and/or implement updates to determinedrisk indices due to receipt of additional information (e.g., as the usercontinues to drive a vehicle, or as biometric data of the user continuesto be received). In some examples, risk index update module 214 mayreceive updates from risk information sources at constant rates. Inother examples, risk index update module 214 may receive updates uponthe occurrence of an event, e.g., a user manual entry of data. Once newinformation is received, a new risk index may be determined, calculated,and/or evaluated. As discussed above, the risk indices may includeadditional risk information, such as an impact score and riskprobability, as desired. In some examples, the risk index may be for aparticular individual or user account. That is, data for each user maybe used to determine a risk index for the user that may then betransmitted to the user account.

As discussed above, the rate at which risk indices are updated (e.g.,based on receipt of additional data or information) may be based on avariety of factors, such as a source of the risk information, devicecapabilities, a type of risk data, a related confidence level of therisk information, e.g., based on a reliability of an associated riskinformation source, privacy considerations and restrictions, and thelike. Further, various algorithms may be used to determine the influenceof updated information on a risk index. The risk index update module 214may utilize one or more machine learning tools such as, for example, alinear regression, a decision tree, a support vector machine, a randomforest, a k-means algorithm, gradient boosting algorithms,dimensionality reduction algorithms, and so forth. For example, riskindex update module 214 may be provided with training data comprisinginformation related to one or more risk characteristics, andapplications or services that have been determined to share such riskcharacteristics. Accordingly, risk index update module 214 may betrained, for example, via supervised learning techniques, based on suchlabeled data, to learn an association between the one or more riskcharacteristics and applications or services. Based on such information,risk index update module 214 may be trained to determine appropriateupdates to one or more risk indices.

In some instances, risk information may be unstructured, and acombination of supervised and semi-supervised learning techniques may beutilized to train a machine learning model of risk index update module214. For example, the risk index update module 214 may be configured todetect patterns in risk information, and apply these patterns to detecta type of risk data being collected. Also, for example, the risk indexupdate module 214 may be configured to detect patterns between types ofapplications or services. The risk index update module 214 may beconfigured to analyze such and other factors, determine patterns, anddetermine clusters based on such patterns. In some embodiments, anoutput of the risk index update module 214 may be reviewed by a humanoperator. Accordingly, the human operator may confirm the analysis ormodify it, and this may form additional training data for the risk indexupdate module 214.

In some embodiments, the risk index update module 214 may generatefeature vectors indicative of the one or more risk characteristics of anapplication or service. Such feature vectors may be compared, based onsimilarity or other distance measuring metrics, to determineapplications or services that are proximate to each other. Accordingly,applications or services may be clustered based on similarity of the oneor more risk characteristics.

Accordingly, one or more sensors 216 a, 216 b, and 216 c (collectivelyreferred to as sensors 216) may be used to obtain data that may be usedto obtain updated risk information associated with the user. Forinstance, the one or more sensors may include sensors to detect drivingbehaviors of the user, such as hard braking, speeding, and the like. Inanother example, one or more sensors may be used to detect biometricinformation of a user such as heat rate, blood pressure, temperature, orthe like. In another example, one or more sensors may be used to detectenvironmental conditions such as precipitation, humidity, cloud cover,or the like. In still another example, one or more sensors may be usedto determine road conditions or to obtain information from outsidesources (e.g., external databases, or the like) regarding trafficconditions, types of road (e.g., two-lane road, four-lane road), speedlimit of the road, or the like. The data from one or more sensors 216,which may include data from combinations of different types of sensors,may be used by the risk index update module 214 to determine an updatedrisk index for the user.

In examples in which the insurance premium is determined based ontraditional policy factors (either in combination withrisk-index-focused factors or alone) the traditional policy factors,such as driving record, credit information, driving experience, vehiclefeatures and/or specifications, coverages, deductibles, policy limits,etc. may be obtained from, for example, policy module 208. In someexamples specific to vehicles, a risk index may be determined orcalculated for a particular trip. Additionally or alternatively, therisk index may be calculated or determined in real-time or nearreal-time, such that the risk index may change as the user's drivingbehavior changes, as the type of road changes, as the environmentalconditions change, or the like. Thus, for example, if a user is drivingat speed higher than the speed limit and it is raining, the risk indexmay be higher than if the user is driving at the speed limit and/orthere is no precipitation. This is merely one example of how risk indexmay change based on received sensor data or other information and shouldnot be viewed as limiting the disclosure to only this example. Rather,various other changes in received sensor data or other information maybe used to modify or alter the risk index.

Similar to the risk index account information, the risk index update maybe displayed to a user, such as via one or more computing devices 212a-212 f. In some examples, the risk index update module 214 may generateand/or display to a user suggestions for improving the risk index byapplying the machine learning model to risk information associated withthe user account. For instance, the system may generate, using themachine learning model an alternate route that has been determined to besafer than the user's current route and, thus, by taking the alternateroute, the risk index may be reduced. In another example, a user may bedriving faster than a posted speed limit. The system may generate anotice to display to the user (e.g., via a computing device 212 a-212 f)indicating that, by slowing down, the user's risk index may be reduced.These are merely some examples of messages that may be displayed inorder to aid the user in reducing the risk index relating to a useraccount. However, various other suggestions or behavior modificationsmay be generated and provided to the user without departing from thescope of the present disclosure.

The risk index based insurance system 200 may further include a riskindex marketplace 218. The risk index marketplace 218 may be connectedto or in communication with various other modules within the risk indexbased insurance system 200. In some examples, the risk index marketplace218 may be used to update one or more aspects of a user's account. Forinstance, upon the user reaching a predetermined low risk thresholdwithin the user account of the user (e.g., the risk index within theaccount reaches a certain low risk threshold) the user may be notifiedthat the risk index in the account is low and may offer one or moreoptions for purchasing an insurance premium at a discounted rate in theaccount. In some examples, the user may receive a pre-notification, suchthat as the risk index approaches the threshold (e.g., is within asecond threshold of the predetermined threshold) a notification may begenerated and/or transmitted to a user (e.g., via a mobile device,on-board vehicle display, or the like). As another example, upon theuser reaching a predetermined high risk threshold within the useraccount of the user (e.g., the risk index within the account reaches acertain high risk threshold) the user may be notified that the riskindex in the account is high and may offer one or more recommendation tolower the risk threshold, and one or more options for purchasingadditional insurance coverage in the account.

For example, in some instances, upon reaching a low threshold risk indexwithin the account, a notification may be displayed to the user (e.g.,via one or more of computing devices 212 a-212 f) indicating that theuser account is approaching a level at which a risk index is considered“low” and offering an additional discounted insurance premium and/orproviding a notification that the risk index is approaching a certainlow risk threshold. In some instances, upon reaching the high thresholdrisk index within the account, a notification may be displayed to theuser (e.g., via one or more of computing devices 212 a-212 f) indicatingthat the user account is approaching a level at which a risk index isconsidered “high” and providing one or more recommendation to lower therisk threshold, offering additional insurance coverage and/or providinga notification that the risk index is approaching a certain high riskthreshold. In some examples, the user may store credit card or otherpayment information (e.g., account information, debit card information,electronic funds transfer information, and the like) in the system(e.g., within the risk index marketplace 218) such that, upon receivingthe notification, the user may select a “purchase” option and the newinsurance premium may be purchased by the user and charged to the storedpayment information. In some arrangements, a user may input paymentinformation (e.g., credit card information, debit card information,checking or other account information, electronic funds transferinformation, and the like) and may identify a predetermined thresholdbelow which the system may automatically purchase anew insurancepremium. These and various other arrangements will be discussed morefully below.

The risk index marketplace 218 may also provide insurance premiums forsale to other users based on associated risk indices. For instance, auser may obtain insurance through a different insurance provider.Accordingly, users having insurance policies with other providers maypurchase premiums from the risk index marketplace 218 and may have therisk indices applicable to an account associated with the policyprovided by or associated with the other insurance provider. In someexamples, entity 201 may charge a service fee or surcharge for purchaseof premiums associated with a policy provided by another insurancecarrier.

The display generating module 210 may generate one or more userinterfaces, as described herein. Once a user interface is generated, itmay be transmitted to one or more computing devices for display. Forinstance, a user interface may be transmitted to one or more of asmartphone 212 a, personal digital assistant (PDA) 212 b, tablet 212 c,laptop 212 d, or other computing device 212 e. In some examples, theinterface(s) may be displayed to a user on a vehicle display 212 f, suchas a dashboard display device of an on-board vehicle computing device.In some examples, one or more generated user interfaces, recommendationsfor reducing a risk index, or the like, may be displayed via the vehicledisplay 212 f and may be transmitted to a second computing device (e.g.,one of 212 a-212 e). However, in some examples, the system might notdisplay the interface or notification on the second computing device 212a-212 e until the system determines that an occurrence or event hasended. This may aid in providing the notification to the user on thesecond computing device in a more timely and efficient manner.

FIG. 3 illustrates a block diagram of risk index determination andanalysis system 300 including additional aspects of the risk index basedinsurance system 200 shown in FIG. 2 and/or implementing the risk indexbased insurance system 200 of FIG. 2 , in accordance with one or moreaspects described herein. The system includes a vehicle 310, a usercomputing device 330, a home system 340, an insurance system server 350,and additional related components. As discussed below, the components ofthe system 300, individually or using communication and collaborativeinteraction, may determine, present, and implement various types of riskindex based insurance to customers, including providing or facilitatingpurchase of a risk index based insurance policy and/or associated riskindices, determining an initial risk index or an updated risk index,communicating a risk index to a user, generating and providingsuggestions to a user for reducing risk index, etc. To perform suchfeatures, the components shown in FIG. 3 each may be implemented inhardware, software, or a combination of the two. Additionally, eachcomponent of the system 300 may include a computing device (or system)having some or all of the structural components described above for therisk index determination device 100.

Vehicle 310 in the system 300 may be, for example, an automobile, amotorcycle, a scooter, a bus, a recreational vehicle, a boat, or othervehicle for which vehicle data, location data, driver data (or operatordata), operational data and/or other driving data (e.g., location data,time data, weather data, etc.) may be collected and analyzed. Thevehicle 310 includes vehicle operation sensor 311 (similar to one ormore of sensors 216 a-216 c of FIG. 2 ) capable of detecting andrecording various conditions at the vehicle and operational parametersof the vehicle. For example, sensor 311 may detect and store datacorresponding to the vehicle's location (e.g., GPS coordinates), time,travel time, speed and direction, rates of acceleration or braking, gasmileage, and specific instances of sudden acceleration, braking,swerving, and distance traveled. Sensor 311 also may detect and storedata received from the internal system of vehicle 310, such as impact tothe body of the vehicle, air bag deployment, headlights usage, brakelight operation, door opening and closing, door locking and unlocking,cruise control usage, hazard lights usage, windshield wiper usage, hornusage, turn signal usage, seat belt usage, phone and radio usage withinthe vehicle, autonomous driving system usage, maintenance performed onthe vehicle, and other data collected by the vehicle's computer systems,including the vehicle on-board diagnostic systems (OBD).

Additional sensors 311 may detect and store the external drivingconditions, for example, external temperature, rain, snow, light levels,and sun position for driver visibility. For example, external camerasand proximity sensors 311 may detect other nearby vehicles, vehiclespacing, traffic levels, road conditions, traffic obstructions, animals,cyclists, pedestrians, and other conditions that may factor into adriving data/behavior analysis. Sensor 311 also may detect and storedata relating to moving violations and the observance of traffic signalsand signs by the vehicle 310. Additional sensors 311 may detect andstore data relating to the maintenance of the vehicle 310, such as theengine status, oil level, engine coolant temperature, odometer reading,the level of fuel in the fuel tank, engine revolutions per minute(RPMs), software upgrades, and/or tire pressure.

Vehicles sensor 311 also may include cameras and/or proximity sensorscapable of recording additional conditions inside or outside of thevehicle 310. For example, internal cameras may detect conditions such asthe number of the passengers and the types of passengers (e.g. adults,children, teenagers, pets, etc.) in the vehicles, and potential sourcesof driver distraction within the vehicle (e.g., pets, phone usage, andunsecured objects in the vehicle). Sensor 311 also may be configured tocollect data identifying a current driver from among a number ofdifferent possible drivers, for example, based on driver's seat andmirror positioning, driving times and routes, radio usage, etc.Voice/sound data along with directional data also may be used todetermine a seating position within a vehicle 310. Sensor 311 also maybe configured to collect data relating to a driver's movements or thecondition of a driver. For example, vehicle 310 may include sensors thatmonitor a driver's movements, such as the driver's eye position and/orhead position, etc. Additional sensors 311 may collect data regardingthe physical or mental state of the driver, such as fatigue orintoxication. The condition of the driver may be determined through themovements of the driver or through other sensors, for example, sensorsthat detect the content of alcohol in the air or blood alcohol contentof the driver, such as a breathalyzer, along with other biometricsensors.

Certain vehicle sensors 311 also may collect information regarding thedriver's route choice, whether the driver follows a given route, and toclassify the type of trip (e.g. commute, errand, new route, etc.) andtype of driving (e.g., continuous driving, parking, stop-and-go traffic,etc.). In certain embodiments, sensors 311 and/or cameras may determinewhen and how often the vehicle 310 stays in a single lane or strays intoother lane. A Global Positioning System (GPS), locational sensorspositioned inside the vehicle 310, and/or locational sensors or devicesexternal to the vehicle 310 may be used to determine the route, speed,lane position, road-type (e.g. highway, entrance/exit ramp, residentialarea, etc.) and other vehicle position/location data.

The data collected by vehicle sensor 311 may be stored and/or analyzedwithin the vehicle 310, such as for example a driving analysis module314 integrated into the vehicle, and/or may be transmitted to one ormore external devices. For example, as shown in FIG. 3 , sensor data maybe transmitted via a telematics device 313 to one or more remotecomputing devices, such as user computing device 330, insurance systemserver 350, and/or other remote devices.

As shown in FIG. 3 , the data collected by vehicle sensor 311 may betransmitted to an insurance system server 350, user computing device330, and/or additional external servers and devices via telematicsdevice 313. Telematics device 313 may be one or more computing devicescontaining many or all of the hardware/software components as the riskindex determination device 100 depicted in FIG. 1 . As discussed above,the telematics device 313 may receive vehicle operation data and drivingdata from vehicle sensor 311, and may transmit the data to one or moreexternal computer systems (e.g., insurance system server 350 of aninsurance company, financial institution, or other entity) over awireless transmission network. Telematics device 313 also may beconfigured to detect or determine additional types of data relating toreal-time driving and the condition of the vehicle 310. The telematicsdevice 313 also may store the type of vehicle 310, for example, themake, model, trim (or sub-model), year, and/or engine specifications, aswell as other information such as vehicle owner or driver information,insurance information, and financing information for the vehicle 310.

In the example shown in FIG. 3 , telematics device 313 may receivevehicle driving data from vehicle sensor 311, and may transmit the datato an insurance system server 350. However, in other examples, one ormore of the vehicle sensors 311 or systems may be configured to receiveand transmit data directly from or to an insurance system server 350without using a telematics device. For instance, telematics device 313may be configured to receive and transmit data from certain vehiclesensors 311 or systems, while other sensors or systems may be configuredto directly receive and/or transmit data to an insurance system server350 without using the telematics device 313. Thus, telematics device 313may be optional in certain embodiments.

The system 300 in FIG. 3 also includes a user computing device 330. Usercomputing device 330 may be, for example, a personal computer, a laptop,a smartphone or other mobile phone device, a tablet computer, and thelike, and may include some or all of the elements described above withrespect to the risk index determination device 100. As shown in thisexample, some user computing devices in system 300 (e.g., user computingdevice 330) may be configured to establish communication sessions withvehicle-based devices and various internal components of vehicle 310 viawireless networks or wired connections (e.g., for docked devices),whereby such user computing devices 330 may have secure access tointernal vehicle sensors 311 and other vehicle-based systems. However,in other examples, the user computing device 330 might not connect tovehicle-based computing devices and internal components, but may operateindependently by communicating with vehicle 310 via their standardcommunication interfaces (e.g., telematics device 313), or might notconnect at all to vehicle 310.

User computing device 330 may include a network interface 333, which mayinclude various network interface hardware (e.g., adapters, modems,wireless transceivers, etc.) and software components to enable usercomputing device 330 to communicate with insurance system server 350,vehicle 310, home system 340, and various other external computingdevices. One or more specialized software applications, such as ananalysis module 334 and/or a risk index application 335 may be stored inthe memory of the user computing device 330. The analysis module 334 andrisk index application 335 may be received via network interface 333from the insurance server 350, vehicle 310, or other applicationproviders (e.g., application stores). As discussed below, the analysismodule 334 and risk index application 335 may or may not include varioususer interface screens, and may be configured to run as user-initiatedapplications or as background applications. The memory of the usercomputing device 330 also may include databases configured to receiveand store vehicle data, driving data, driving trip data, and the like,associated with one or more drivers and/or vehicles.

Like the vehicle-based computing devices in vehicle 310, user computingdevice 330 also may include various components configured to generateand/or receive vehicle data, driver data, and driving data or otheroperational data. For example, using data from the GPS 332, an analysismodule 334 may be able to identify starting and stopping points ofdriving trips, determine driving speeds, times, routes, and the like.Additional components of user computing device 330 may be used togenerate or receive driving data for the analysis module 334 and/or riskindex application 335, such as an accelerometer, compass, and variouscameras and proximity sensors. As discussed below, these and othercomputing device components may be used to receive, store, and outputvarious user/driver data, to identify starting and stopping points andother characteristics of driving trips, to determine various drivingdata such as speeds, driving routes and times, acceleration, braking,and turning data, and other driving conditions and behaviors. In someimplementations, the analysis module 334 may store and analyze the datafrom various computing device components, and the risk index application335 may use this data, alone or in any combination with other componentsor devices (e.g., insurance server 350), to determine and presentinsurance offers, insurance costs, and the like. The analysis module 334and risk index application 335 may utilize machine learning and weightedequation modeling with the risk information inputs to a risk index.

When user computing devices within vehicles are used to detect vehicledriving data and/or to receive vehicle driving data from vehiclesensors, such user computing devices 330 may store, analyze, and/ortransmit the vehicle driver data (e.g., data identifying a currentdriver), driving data (e.g., speed data, acceleration, braking, andturning data, and any other vehicle sensor or operational data), anddriving trip data (e.g., driving route, driving times, drivingdestinations, etc.), to one or more other devices. For example, usercomputing device 330 may transmit driver data, driving data and drivingbehaviors, and driving trip data directly to one or more insuranceservers 350, and thus may be used in conjunction with or instead oftelematics devices 313. Moreover, the processing components of the usercomputing device 330 may be used to identify vehicle drivers andpassengers, analyze vehicle driving data, analyze driving trips,determine parameters related to aspects of risk index based insurancepolicies, and perform other related functions. Therefore, in certainembodiments, user computing device 330 may be used in conjunction with,or in place of, the insurance system server 350.

Vehicle 310 may include driving analysis module 314, which may beseparate computing devices or may be integrated into one or more othercomponents within the vehicle 310, such as the telematics device 313,autonomous driving systems, or the internal computing systems of vehicle310. As discussed above, driving analysis module 314 also may beimplemented by computing devices independent from the vehicle 310, suchas user computing device 330 of the drivers or passengers, or one ormore separate computer systems (e.g., a user's home or office computer).In any of these examples, the driving analysis module 314 may containsome or all of the hardware/software components as the risk indexdetermination device 100 depicted in FIG. 1 . Further, in certainimplementations, the functionality of the driving analysis module 314,such as storing and analyzing driver data, vehicle data, driving dataand driving behaviors, and determining, presenting, and implementingaspects of risk index based insurance polies, may be performed in aninsurance system server 350 rather than by the vehicle 310 or usercomputing device 330. In such implementations, the vehicle 310 and/oruser computing device 330, might only collect and transmit driver data,vehicle data, driving data, and the like to the insurance server 350,and thus the vehicle-based driving analysis module 314 may be optional.

User computing device 330 may receive information from the vehicle 310,home system 340, and/or insurance system server 350, for use in variousanalyses and displays related to one or more risk indices as describedin further detail herein. Additionally, user computing device mayinclude GPS 332, network interface 333, analysis module 334, risk indexapplication 335, biometrics device 336, health systems module 337,digital systems module 338, and/or financial systems module 339.

Biometrics device 336 may be coupled to health systems module 337 tomeasure and collect various biometric data associated with a user. Insome examples, biometrics device 336 may be used to obtain biometricdata verifying an identity of a user of the user computing device. Thedata may be transmitted to the health systems module 337, which mayprocess real-time and/or near real-time data and then provide theprocessed information in a meaningful way for display via a graphicaluser interface (“GUI”) on the user computing device 330. For example, anelectro-cardiogram (“ECG”) meter may identify a unique ECG signature fora user of user computing device 330, a fingerprint scanner may be usedto obtain the user's fingerprint, a microphone may be used to collect avoiceprint of the user, a camera with facial recognition software may beused to obtain the user's facial features or structure, and/or variousother sensors may be used for collection of various other types ofbiometric data. Physical activity data, such as steps taken in apredetermined time period (e.g., one day, one week, or the like), heartrate (e.g., resting heart rate, elevated heart rate, or the like), bloodpressure, fitness or activity data, oxygen capacity, pulse, and the likemay be captured by the biometrics device 336 and transmitted to thehealth systems module 337 for analysis. In some examples, biometricsdevice 336 may include a wearable device (e.g., fitness trackingdevice), and some data (e.g., step data, activity data, and the like)may be captured by the wearable device and transmitted to the healthsystems module 337 for analysis. In some examples, a request forphysical trait data, e.g., from health systems module 337, may include arequest for the user to capture data such as a current and/or restingheart rate, blood pressure, oxygen consumption, fitness level, or thelike, captured via one or more sensors on or associated with thebiometrics device 336. Additional user data may also be captured via thehealth systems module 337 and biometrics device 336, such as whether auser is a smoker (e.g., based on sensor data, facial analytics, or thelike), blood pressure, diabetes, lung disease, and the like, may bedetermined using one or more sensors associated with biometrics device336. In some examples, blood pressure and/or heart rate may be measuredby measuring the wave form velocity of the blood flow in a user's fingerusing an image capture device of the biometrics device 336. Additionallyor alternatively, user physical (e.g., physical trait data) or biometricdata may be manually input by the user of the user computing device 330.Additionally or alternatively, body mass index may be measured usinguser inputs of height and weight in combination with images of the user.In some examples, images may be captured and machine learning may beused to extract body measurements of the user from the images.

Digital systems module 338 may collect data captured or received from avariety of sources, including third party or external data sources, andmay store user data related to financial transactions, purchase history,internet browsing history, social media data, ride share application orother application usage data, and the like. The data may be receiveddirectly from the user or via a third party. The data may be captured,stored, processed, and the like, with the permission of the user. Insome examples, users may interact with the digital systems module 338,including providing user information and/or preferences, deviceinformation, account information, warning/suggestion messages, etc.Digital systems module 338 may store a user profile for one or moreusers that includes the users' privacy preferences, and may keep anaccount for each user and may identify the user when the user logs in orsubmits other identifying information. In other embodiments, userprivacy preferences may be received by the digital systems module 338when a user sends a request for a product or service. In some examples,digital systems module 338 may generate a user profile using, at leastin part, data received and/or collected therefrom, or received fromexternal sources associated with accounts in the user profile. The userprofile may include details of the user, including but not limited to auser's name, age, address, driver's license number, credit card or bankinformation, insurance policies, networked devices associated with theuser, and privacy preferences, etc. In some examples, users may manuallyenter additional and/or confirm information in their user profilesthrough a mobile application or computing device interface associatedwith the user computing device 330. In some examples, digital systemsmodule 338 may store information associated with various devices (e.g.,laptops, smartphones, tablets, personal computers, PDAs, and the like)linked to a user profile, such as security software updates, passwordprotection settings, detected fraudulent activity, unverified deviceusers, and the like. In some examples, additional information that maybe entered by the user may include financial account details and orverification of online accounts used by a user.

Financial systems module 339 may detect potential fraud in one or moreuser accounts associated with the user profile or associated with one ormore devices linked to the user profile. In some examples, financialsystems module 339 may collect information using the user computingdevice 330 or other device, and may subsequently compare suchinformation to past databases or fraud indicators. If the financialsystems module 339 detects a potential fraud situation, the system mayprevent the user from obtaining further user profile information throughan automated process and may prompt for additional actions such aslocking an associated user account pending further action. Additionalfinancial information may be entered by the user, such as financialaccount details, verification of online accounts used by a user, and thelike. In some examples, the financial systems module 339 may collectinformation from a variety of sources (e.g., credit monitoring services,identity theft protection services, user information protectionservices, etc.), and store the combined information in a database.

Home system 340 may collect various components of information associatedwith a home property, and may send such information to the usercomputing device 330 and/or the insurance system server 350.Additionally, the home system 340 may further include a plurality ofhome sensors 341. For example, the home system 340 may include aplurality of appliances and/or systems and one or more of the appliancesand/or systems (e.g., devices) may be monitored by a plurality of homesensors 341 (e.g., one or more sensing devices). The plurality of homesensors 341 may monitor one or more features in a home and transmitmonitored data to the user computing device 330 and/or the insurancesystem server 350. For example, a home may generally include a varietyof systems, appliances, and the like that may be monitored by theplurality of home sensors 341. The plurality of home sensors 341 mayinclude one or more sensors or sensing devices which may be arranged onor integrated into devices such as hot water heaters, refrigerators,washing machines, dryers, furnaces, air conditioning units, windows(e.g., to detect breakage), pipes (e.g., to detect leakage), utilitiesor utility meters, such as gas, water, and electric, and the like. Forexample, the plurality of home sensors 341 may include at least one ofmotion sensors, water heater sensors, power sensors, moisture sensors,temperature sensors, window sensors, sump pump sensors, heat or smokingsensing devices, presence sensors, float sensors, speed sensors,breakage sensors, cameras, proximity sensors, and the like. The homesensors 341 may include devices for sensing temperature, sewage backup,natural gas, propane, etc., air quality (e.g., carbon monoxide, etc.),air flow quality, water flow, and the like. Additionally, the homesystem 340 may include one or more cameras 342, such as security orother video cameras, live video feeds, and the like, that may receiveand/or transmit video or other image data related to one or moreappliances, systems, etc. In some examples, cameras 342 providing videomonitoring or video feed may be monitoring the premises on a periodic orcontinuous basis. In some arrangements, the cameras 342 providing videomonitoring or video feed may be in communication with one or more othersensing devices and may activate or begin monitoring, providing videofeed, etc. upon an indication received from the one or more othersensing devices. In other examples, the plurality of home sensors 341may include sensors or other monitoring devices that may be arranged onor integrated into paint, bricks or other building materials, and thelike. In yet additional examples, the plurality of home sensors 341 mayinclude “smart” materials, such as smart paints, smart bricks, and thelike, that may provide indications of wear or potential failure.

The system 300 also may include one or more insurance system servers350, containing some or all of the hardware/software components as therisk index determination device 100 depicted in FIG. 1 . The insurancesystem server 350 may include hardware, software, and network componentsto receive driver data, vehicle data, and vehicle operationaldata/driving data from one or more vehicles 310, user computing devices330, and other data sources. The insurance system server 350 may includean insurance database 352 and risk index based insurance system 351 torespectively store and analyze driver data, vehicle data, and drivingdata, etc., received from vehicle 310, user computing device 330, andother data sources. In some examples, the risk index based insurancesystem 351 may include many or all of the components of risk index basedinsurance system 200 described with respect to FIG. 2 .

The insurance system server 350 may initiate communication with and/orretrieve driver data, vehicle data, and driving data from vehicle 310wirelessly via telematics device 313, user computing device 330, or byway of separate computing systems over one or more computer networks(e.g., the Internet). Additionally, the insurance system server 350 mayreceive additional data from other third-party data sources, such asexternal traffic databases containing traffic data (e.g., amounts oftraffic, average driving speed, traffic speed distribution, and numbersand types of accidents, etc.) at various times and locations, externalweather databases containing weather data (e.g., rain, snow, sleet, andhail amounts, temperatures, wind, road conditions, visibility, etc.) atvarious times and locations, and other external data sources containingdriving hazard data (e.g., road hazards, traffic accidents, downedtrees, power outages, road construction zones, school zones, and naturaldisasters, etc.), route and navigation information, and insurancecompany databases containing insurance data (e.g., driver score,coverage amount, deductible amount, premium amount, insured status) forthe vehicle, driver, and/or other nearby vehicles and drivers.

Data stored in the insurance database 352 may be organized in any ofseveral different manners. For example, a driver table in database 352may contain all of the data for users associated with the insuranceprovider (e.g., customer personal information, insurance accountinformation, demographic information, accident histories, risk factors,property scores, driving scores and driving logs, etc.), a vehicle tablemay contain all of the vehicle data for vehicles associated with theinsurance provider (e.g., vehicle identifiers, makes, models, years,accident histories, maintenance histories, travel logs, estimated repaircosts and overall values, etc.), and a driving trip table may store allof the driving trip data for drivers and vehicles associated with theinsurance provider (e.g., driving trip driver, vehicle driven, triptime, starting and ending points, route driven, etc.). Other tables inthe database 352 may store additional data, including data typesdiscussed above (e.g. traffic information, road-type and road conditioninformation, weather data, insurance policy data, etc.). Additionally,one or more other databases of other insurance providers containingadditional driver data and vehicle data may be accessed to retrieve suchadditional data.

The risk index based insurance system 351 within the insurance systemserver 350 may be configured to retrieve data from the database 352, ormay receive driver data, vehicle data, and driving trip directly fromvehicle 310, user computing device 330, or other data sources, and mayperform driving data analyses, determine insurance parameters for riskunit based insurance policies, and other related functions. Thefunctions performed by the risk index based insurance analysis system351 may be performed by specialized hardware and/or software separatefrom the additional functionality of the insurance system server 350.Such functions may be similar to those of driving analysis module 314 ofvehicle 310, and the analysis module 334 and risk index application 335of user computing device 330, and further descriptions and examples ofthe algorithms, functions, and analyses that may be executed by the riskindex based insurance system 351 are described below.

In various examples, the driving data and driving trip analyses and/orrisk index based insurance determinations may be performed entirely inthe insurance system server 350, may be performed entirely in thedriving analysis module 314, or may be performed entirely in theanalysis module and risk index application 335 of user computing device330. In other examples, certain analyses of driver data, vehicle data,and driving trip data, and certain risk unit based insurancedeterminations may be performed by vehicle-based devices (e.g., withindriving analysis module 314) or user computing device 330 (e.g., withinanalysis module 334 and risk index application 335), while other dataanalyses and risk index based insurance determinations are performed bythe risk unit based insurance system 351 at the insurance system server350. For example, a vehicle-based driving analysis module 314, or thehardware and software components of user computing device 330 maycontinuously receive and analyze driver data, vehicle data, driving tripdata, and the like to determine certain events and characteristics(e.g., commencement of a driving trip, identification of a driver,determination of a driving route or intended destination, driving dataand behaviors during driving trips, etc.), so that large amounts of dataneed not be transmitted to the insurance system server 350. However, forexample, after a driver, vehicle, and/or driving trip is determined by avehicle-based device and/or mobile device, corresponding information maybe transmitted to the insurance server 350 to perform insurance offerand cost determinations, determine updates to risk indices, generate oneor more recommendations for reducing a risk index, etc. which may betransmitted back to the vehicle-based device and/or user computingdevices.

The steps that follow in FIG. 4 may be implemented by one or more of thecomponents in FIGS. 1 through 3 and/or other components, including othercomputing devices. FIG. 4 illustrates an exemplary method 400 forgenerating and displaying an interactive risk index dashboard inaccordance with one or more aspects described herein. In that regard,and as described above for FIGS. 1 through 3 , the system may include atleast one processor, a communication interface communicatively coupledto the at least one processor, and memory storing computer-readableinstructions that, when executed by the processor, cause the system toperform a number of step, such as those shown in FIG. 4 .

Initially, at step 405, risk information is received, e.g., at the riskindex determination device 100, from a plurality of risk informationsources. In some instances, the initial setup for a user to determineone or more indices may include a display of a setup user interface, inwhich a user device may display a graphical user interface similar tographical user interface 600, which is shown in FIG. 6 . For example,the user device may display a user interface that notifies the user thata user account is not presently set up for generating one or more riskindices for a user account, and then provide the user with the option tocreate a user account and provide information for generating anddisplaying one or more risk indices. In that regard, an initial set ofrisk indices may be determined by a setup process, e.g., by linking oneor more user accounts or user devices to provide risk information to therisk index determination device 100. Accordingly, by displaying thesetup interface, the user may be given access to review various dataassociated with one or more risk indices and the related riskinformation as data is received and analyzed.

Referring again to FIG. 4 , risk information data associated with a useris initially received from at a plurality of risk information sources atstep 405. In some examples, step 405 may include receiving, from aplurality of risk information sources, risk information associated witha user account, where the risk information includes a plurality of riskcomponents. The plurality of risk information sources may include datareceived from a number of other applications or devices, such asbiometrics device 336, health systems module 337, digital systems module338, financial systems module 339, vehicle sensors 311, telematicsdevices 313, home sensors 341, cameras 342, and the like.

Step 405 may include the retrieval of various types of data from one ormore external devices. In some examples, the risk information may bereceived from a user and may be received, via a computing device (e.g.,mobile device, or the like). The information may include informationassociated with the user, such as name, contact information, healthinformation, property information, financial account information,digital property information, vehicle information, and the like. In someexamples, a specific risk index may be determined based on one or moreaspects of an insurance policy, e.g., a level of coverage. For instance,similar to conventional insurance policies, a user may select fromdifferent levels of protection (e.g., whether to include collisioncoverage, amount of coverage for personal property, and the like).Similarly, a risk index may be influences by a related amount of policycoverage. In another example, different levels of coverage selected maybe reflected in various aspects of a risk index, or by presenting aplurality of risk indices for a given risk component. For instance, therisk index may vary based on a level of coverage selected. Althoughdifferent levels of coverage may be available to a user, the levelsoffered may meet minimum standards for insurance coverage, such as thoserequired by the state in which the user lives, or the like.

In some examples, the risk information sources may include user input,biometric devices, computing devices, geo-location devices, enterpriseservers providing financial, insurance, health, vehicle, home, or otherrelated data, user account servers, vehicle sensors or telematicsdevices, home or property devices, Internet of Things (IOT) devices,cameras, thermostats, police servers, weather servers, insurance systemservers, financial system servers, traffic system servers, crime systemservers, one or more user calendars, e.g., for detection of plannedfuture occurrences, and the like.

At step 410, a risk index may be determined. As discussed above, therisk index may provide a quantification of a relative risk of an aspectassociated with a user account. For instance, the risk index may includean impact score and a risk probability. A risk index may be determinedfor each risk component associated with a user account and may be usedto provide risk index based insurance policies in which, a variable riskscore influences an insurance premium that may be offered to the user.Step 410 may thus include determining an impact score and a riskprobability for each of the plurality of risk components by applying amachine learning model to risk information associated with the useraccount. The determining may include using weighting and machinelearning models and/or algorithms to arrive at the relevant riskindices, impact scores, and/or risk probabilities. In some instances,the risk index determination device 100 may be configured to dynamicallytune the machine learning models and/or algorithms based on receivedfeedback and/or as additional data is received from the various riskinformation sources. In some instances, the risk index determinationdevice 100 may use a machine learning model to compute the risk indexusing weighted average scores, e.g., by maintaining a plurality ofweighting values to be applied to each of the plurality of riskcomponents. For example, risk index determination device 100 may utilizeone or more machine learning tools such as, for example, a linearregression, a decision tree, a support vector machine, a random forest,a k-means algorithm, gradient boosting algorithms, dimensionalityreduction algorithms, and so forth, to determine one or more relevantrisk indices, impact scores, and/or risk probabilities. Additionally, aconfidence level for each of the plurality of risk components may bedetermined. The confidence level may relate to a reliability of anassociated risk information source.

At step 415, an interactive risk dashboard may be generated using thedetermined risk index. In some examples, a risk index based insurancepolicy may be generated for the user and a risk index account may becreated for the user as part of step 415. The risk index account may beassociated with the user or with a group of users, e.g., a family or abusiness. Generating the interactive risk dashboard may includegenerating a plurality of interactive risk component elements, eachassociated with a corresponding risk component of the plurality of riskcomponents.

In some examples, a projected risk probability of a future riskoccurrence may be calculated, determined, and/or evaluated based on theplurality of risk components and one or more variable settings.Accordingly, a plurality of projected risk probabilities may be providedwith the generated interactive risk dashboard based on a plurality ofthe one or more variable settings.

In some examples, generating the interactive risk dashboard, orgenerating a portion of the interactive risk dashboard may includegenerating a first offer for a risk index-based insurance policy basedon at least one of the plurality of risk components and currentinformation associated with the at least one of the plurality of riskcomponents, and generating a second offer for a risk index-basedinsurance policy based on the at least one of the plurality of riskcomponents and based on the user completing a risk reductionrecommendation associated with the at least one of the plurality of riskcomponents. Accordingly, the first offer, the second offer, and the riskreduction recommendation may be displayed with a respective interactiverisk component element or may be provided in a sub-display of theinteractive risk dashboard upon a user interaction with a respectiveinteractive risk component element.

At step 420, the interactive risk dashboard may be displayed to adisplay of the risk index determination device 100. Data related tovarious behaviors and conditions and/or traditional risk data, may becombined to determine the risk index in real-time or near real-time,e.g., as the user is operating a vehicle. Accordingly, the system mayprovide information associated with the risk index to the user. Forinstance, the vehicle display or mobile device of the user may displaythe current risk index. In another example, the display may includehistorical information associated with risk index based on previousinformation and/or a graphical display of previous and/or current riskindices. Displaying the interactive risk dashboard may includedisplaying each of the plurality of interactive risk component elementsin a portion of the interactive risk dashboard in accordance with arespective determined impact score and risk probability. In someexamples, the plurality of interactive risk component elements may bedisplayed in a grid format in the interactive risk dashboard, such thata first axis in the grid format displays the impact score and a secondaxis in the grid format displays the risk probability. In some examples,a total risk index based on the plurality of risk components may also bedetermined and subsequently displayed in a portion of the interactiverisk dashboard. The total risk index may be a product of the riskprobability and the impact score. In some examples, the total risk indexmay be the result of various machine learning tools or other algorithmsassociated with one or more types of risk or user data.

In some examples, a user prompt requesting additional risk informationassociated with the user account may also be presented in the displayedinteractive risk dashboard. Such a user prompt may be provided, e.g., inresponse to determining information is lacking, or that a risk scorecould be improved with the provision of additional user information.Subsequently, user input may be received providing the requestedadditional risk information, and at least one of the interactive riskcomponent elements may be updated in the interactive risk dashboard inaccordance with the received user input.

At step 425, the risk index determination device 100 may receive a userinteraction with a portion of the interactive risk dashboard. In someexamples, the interaction may be with a portion correspondence to acertain risk element or risk component displayed within the interactiverisk dashboard. In some examples, the interaction may be with a titleportion, a summary portion, or a sidebar portion of the interactive riskdashboard.

At step 430, a sub-display may be provided in the interactive riskdashboard in accordance with the user interaction. In some examples,additional information associated with the selected portion of theinteractive risk dashboard may be displayed based on the userinteraction. In some examples, one or more of the plurality ofinteractive risk component elements in the interactive risk dashboardmay each include one or more components of additional information thatmay be displayed with a respective interactive risk component element orprovided in a sub-display of the interactive risk dashboard upon a userinteraction with a respective interactive risk component element. Insome examples, the one or more components of additional information mayinclude at least one of: a descriptor, a categorical indicator, a riskcoding indicator, or a risk reduction recommendation. The categoricalindicator may include at least one of: finance, health, property (homeand/or vehicle), digital, and the like. The risk reductionrecommendation may include at least one of: a safer routing, a driveralerting, a safe parking alternative, and the like. The risk reductionrecommendation may include guidance or recommendations to reduce arelated risk index, a projected change in at least one of the impactscore or the risk probability associated with the risk reductionrecommendation, and/or an option to automatically modify an operation ofa device or a vehicle, e.g., while an event is in progress as part ofaccepting the recommendation.

Accordingly, one or more aspects of the systems and methods describedherein may be used to address technical difficulties associated withevaluation of various complex and differing types of risks. Byincorporating machine learning models and techniques, the process ofevaluating differing risks may be automated, quantified, and ultimatelyused for various discounts, awards, and/or recommendations. In doing so,one or more of the systems and methods described herein may conserveprocessing resources in risk index generation (e.g., by maintaining useraccount-specific information related to a risk score and/or by onlyprompting for generation of a risk index dashboard at certain intervals)and in the calculation of rates, premiums, discounts, targetedadvertisements, or the like. Furthermore, one or more of the systems andmethods described herein may provide context in which to view varyingrisk indices in a single, cohesive interactive dashboard.

In some examples, the risk index, the impact score and/or the riskprobability may be automatically updated for an associated riskcomponent upon receiving updated risk information from an associatedrisk information source. In some examples, the risk index, the impactscore and/or the risk probability may be updated upon determining thatan event associated with a risk element has been completed.

In some examples an alert may be provided upon determining that a riskindex (and/or the impact score and/or the risk probability) of a riskcomponent is above or meets a threshold for an associated risk componentelement.

In at least one example, the system that performs the various step,e.g., as shown in FIG. 4 may include a first computing device in signalcommunication with at least one other computing device. The firstcomputing device may receive, from the at least one other computingdevice, risk information associated with a user account, wherein therisk information includes a plurality of risk components, e.g., as partof step 405. A second computing device may be provided that is in signalcommunication with the first computing device. The second computingdevice may be configured to record sensor data using at least onesensor, where the sensor data is associated with a risk component of theplurality of risk components. The second computing device may thentransmit the sensor data to the first computing device. In someexamples, the at least one sensor of the second computing device mayinclude a biometric device and the sensor data may include biometricdata relating to a user associated with the user account. In someexamples, the at least one sensor may include a telematics device, andthe sensor data may include vehicle data relating to a vehicleassociated with the user account.

Since an enterprise organization's mission may be to protect forproperty or life uncertainties, a risk index application, such as thosediscussed herein may offer safety services that are personalized andlocation aware, such as safe routing, driver alerting, safe parking fordriving use cases, and the like.

In some examples, such an application may offer safety services for avariety of use cases beyond driving, for example, safety for exercisingoutdoors (where is it safe to run, walk), relevant for life insurance,safety services for homes/properties, weather alerts, other relevantalerts, and the like. Such a holistic application may help with safetyin general, and/or may help users prevent and deal mitigateuncertainties.

The arrangements described herein provide numerous advantages. Forinstance, uncertainties may be prevented based on understanding andpersonalizing risks, including with location awareness. Additionally,when dealing with accidents as they happen, such an application may behelpful in logging various information. For example, if a user's houseis damaged from a storm, the application may provide simple andintuitive functionality to log all relevant information.

In some examples, the application may be connected to a variety of othersensors, such as wearable sensors, house IOT devices, thermostats,cameras, and the like, to collect data relevant for assessing one ormore risk indices.

Lastly, safety services for privacy may be offered by the sameapplication, including online privacy and also protecting the privacy ofthe data collected about the user, and such data may be shared only ifthe user has agreed when understood the balance between privacy andutility.

FIG. 5 illustrates an exemplary flowchart 500 for generating a “what-if”scenario risk estimation of one or more future risk occurrence orforecasted events by applying a machine learning model to riskinformation associated with a user account in accordance with one ormore aspects described herein. In that regard, methods may additionallyinclude computing a “what-if” scenario risk estimation of one or moreforecasted events by applying the risk index determination device 100 todetermine one or more probabilities of certain outcomes, such as shownin FIG. 5 . This may include receiving a request for a “what-if”scenario estimation at step 505. The request may indicate a singlescenario, a collection of scenarios (e.g., with varying factors for acommon event), several different scenarios, and the like. The method maythen include collecting information associated with the specified one ormore “what-if” scenarios at step 510. The collected information mayinclude information associated with the user account, e.g., from aplurality of risk information source that provide risk informationassociated with a user account. The collected information may includeinformation specific to the one or more “what-if” scenarios, but generalto any user. In some examples, collected information may include useraccount-specific information and scenario-specific information.Subsequently, the method may include calculating a risk score for each“what-if” scenario by applying a machine learning model to riskinformation associated with the user account at step 515. The risk scoremay include a compilation of risk indices associated with a respectivescenario. At step 520, the results of the calculated risk score orscores, and, in some examples, additional related risk informationand/or risk indices, are displayed to a user display. At step 525, arisk result recommendation may be provided to a user, e.g., in responseto a user interaction with a portion of the results displayed at step520. The risk result recommendation may include a selection of onescenario from a series of scenarios, e.g., associated with a lowest riskscore. In some examples, the risk result recommendation may include asuggestion or a tip for a user to reduce a risk score associated with agiven scenario. In some examples, the risk result recommendation mayinclude an option to purchase an insurance plan or other product toreduce or mitigate a potential risk associated with a given scenario.

As an example of a “what-if” scenario risk estimation of one or moreforecasted events applicable to methods discussed with respect to FIG. 5, a user may wish to run a “‘What-if’ scenario estimation” based onwanting to go on a trip and wanting to know risks associated with one ormore trip destinations, modes of getting to a destination, timing of thetrip, and the like. In that regard, the user can specify one or moredestinations, whether the user is interested in traveling by air, car,plane, train, boat, and the like, one or more travel windows, one ormore details associated with trip activities, and the like as part ofstep 505. At step 510, the risk index determination device 100 maycollect various information related to the user, e.g., user drivingrecords, vehicle records, user health records that may be relevant totraveling, insurance policies associated with the user, whetherinsurance policies are up to date, credit fraud reports associated withthe user's financial records, and the like. The risk index determinationdevice 100 may collect various information related to the one or moredestinations, e.g., weather records and forecasts, current events, crimereports, health risks, access to emergency care, access to health care,and the like. The risk index determination device 100 may thendetermine, e.g., using machine learning and weighted equation modeling,risk scores associated with each scenario based on the variousinformation, and may then present the results to the user via a userdisplay, e.g., display device 117. A user may interact with thedisplayed results, e.g., by selecting one of the results. Selection of aresult may then provide a risk result recommendation. For example, if auser selects to drive to a destination using his vehicle, the riskresult recommendation may include a recommendation to get vehicleservicing before the trip, to renew a vehicle insurance policy, to leavefor the destination at a certain time of day, to take a certain route toget to the destination, and the like.

FIGS. 6-8 illustrate exemplary interactive risk index dashboard userinterfaces in accordance with one or more aspects described herein thatmay provide to a user (e.g., via a vehicle display, mobile device, orother computing device) to provide information associated with one ormore risk indices. FIG. 6 illustrates one example interactive risk indexdashboard user interface 600 that does not yet list any risk indices.The interface 600 includes a grid format for the placement of variousrisk indices therein, with vertical axis 610 representing a calculatedrisk probability and horizontal axis 620 representing a calculatedimpact score. As shown in interface 600, vertical axis 610 displayingprobability may be in the form of a percentage that ranges from 0 to 1.0or that ranges from 0% to 100%. The probability may provide anestimation of the likelihood of a certain risk occurring. Horizontalaxis 620 displaying impact may be in the form of a logarithmic scorethat ranges from 1 to 10,000. The impact may provide a generalindication of a severity of an impact on the user if a certain risk doesoccur. The logarithmic scale may better capture a substantial increasein impact of risk affecting, e.g., a user's life or health as comparedto other, relatively less severe risks, e.g., relating to themaintenance of digital property. Interface 600 displays a blank riskindex dashboard, e.g., before a user has provided relevant user accountinformation or before one or more external devices have been linked to auser account for use in determining one or more risk indices. In someexamples, the blank risk index dashboard of interface 600 may alsoinclude a prompt 630 to a user to complete a setup process fordetermining one or more risk indices to be display in the risk indexdashboard.

FIG. 7 illustrates one example interactive risk index dashboard userinterface 700 that includes a plurality of risk indices, e.g., after auser has completed a setup process and/or linked the risk indexdashboard to one or more external devices for use in determining one ormore risk indices. Similar to interface 600, interface 700 includes agrid format for the placement of various risk indices therein, withvertical axis 710 representing a calculated risk probability andhorizontal axis 720 representing a calculated impact score. A pluralityof determined risk indices are shown in interface 700 in the gridformat, as described, to graphically show relative impacts andprobabilities of each of the risk indices. As shown in the interface700, each of the risk indices may include a categorical indicator (e.g.,“Finance,” “Health,” “Digital,” “Property,” “Life,” “Family”) and/or adescriptor (e.g., “Bills past due,” “Fraud alerts,” “Elevated bloodpressure for 6 continuous days,” “Hail storm expected tomorrow”). Therisk indices may have a risk coding indicator (e.g., using variouscolors, texts, symbols, icons, and the like) that depict a relativeseverity, score, or other descriptor relating to the risk of a riskindex. For example, risk coding indicators may employ a series of colorsfrom green (low risk) to yellow (moderate risk) to red (high risk). Asanother examples, risk indices affecting health and personal safety mayinclude a person icon, risk indices affecting vehicle may include a caricon, risk indices affecting digital property may have a computer icon,risk indices affecting finances may have a dollar sign icon, and thelike.

FIG. 8 illustrates one example interactive risk index dashboard userinterface 800 that includes additional risk information and a riskreduction recommendation option, e.g., upon a user selecting a givenrisk index portion of interface 700. Similar to interface 600 andinterface 700, interface 800 includes a grid format for the placement ofvarious risk indices therein, with vertical axis 810 representing acalculated risk probability and horizontal axis 820 representing acalculated impact score. Interface 800 shows a selected risk index box830 highlighted and an additional risk information box 840 overlaid onthe grid of interface 800. For example, upon a user selecting a riskindex box 830 for “Hail storm expected tomorrow,” then additional riskinformation box 840 appears and indicates “The weather forecast in yourarea predicts a strong hail storm tomorrow between 2:00 PM and 7:00 PM.This storm has a risk probability of 0.6 and an impact score of 1,000for a total risk score of 600. Take action to reduce risks for thisevent.” Thus, the additional risk information may provide furtherinformation than that displayed in the grid format, may display theexact calculated risk probability and impact score, and/or may include arisk reduction recommendation or link to a risk reductionrecommendation. For example, the text for “Take action to reduce risksfor this event” may include a user selectable link that, upon selection,provides an additional display with tips for reducing property damageduring the hail storm (e.g., parking vehicles in a garage, placement ofstorm shutters over window, covering outdoor furniture, and the like).In some examples, risk recommendations may be performed on the userdevice, e.g., to pay a bill, to renew an insurance policy, to schedule adoctor appointment, to schedule vehicle maintenance, and the like.

Various other risk types and/or risk reduction recommendations may beidentified based on the inputted data from a user, received data fromone or more external devices, known risk factors and algorithms and thelike. The examples described herein are merely some examples and are notintended to limit the risk types or related displayed informationdescribed herein. Rather, various other risks may be identified withoutdeparting from the scope of the present disclosure.

In some examples, risk index determination device 100 may automaticallyprompt, via the interactive risk index dashboard user interface, a userto take a risk reduction action, e.g., upon selection during the set upprocess for such automatic prompts. In some examples, risk indexdetermination device 100 may determine that certain components of a useraccount are lacking, e.g., if a user skipped providing such componentsduring an initial setup process, or if one or more external devices havenot yet been linked to the user account. Accordingly, risk indexdetermination device 100 may automatically prompt, via the interactiverisk index dashboard user interface, a user to complete one or moreadditional steps to provide such missing components to the user account.

In some examples, generating the user interface may further includegenerating one or more recommendations for reducing risk indices,improving likely risks, and the like. The recommendations may bepersonalized for the user in that they are based, at least in part, ondetermined behaviors of the user, historical data of the user or other,e.g., similar users, or the like.

As discussed herein, various aspects of the risk index determination andanalysis, risk probabilities and impact scores, risk reductionrecommendations, notifications, and the like, may be displayed to theuser (e.g., via a computing device display). For instance, items such asrisk probabilities and impact scores, risk reduction recommendations,notifications, and the like, may be displayed. Additionally oralternatively, information such as alerts or notifications of variousrisks, identified risk issues, recommendations for risk scores,recommendations for avoiding risks, alerts regarding dangerous or highrisk situations, and the like, may also be provided to the user via oneor more displays or user interfaces. In some arrangements, particularfactors affecting a risk index may be displayed to the user, such asdriving data, vehicle data, weather forecasts, biometric data, creditreports, and the like. One or more recommendations for improving one ormore risk indices may be provided with the one or more factors.

Further, although various aspects described herein relate to use ofbiometric data, sensor data, vehicle operation data, driving data, etc.,the data collected and used herein may be used with permission of theuser.

Aspects of the invention have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one of ordinary skill in the art willappreciate that the steps illustrated in the illustrative figures may beperformed in other than the recited order, and that one or more stepsillustrated may be optional in accordance with aspects of the invention.

What is claimed is:
 1. An apparatus comprising: a display; one or moreprocessors; and memory storing computer-readable instructions that, whenexecuted by the one or more processors, cause the apparatus to: receive,from a plurality of risk information sources, risk informationassociated with a user account, wherein the risk information includes aplurality of risk components; determine, for each of the plurality ofrisk components, an impact score and a risk probability by applying amachine learning model to risk information associated with the useraccount; generate an interactive risk index dashboard including aplurality of interactive risk index elements, wherein each of theplurality of interactive risk index elements is associated with a riskcomponent of the plurality of risk components; and display, on thedisplay of the apparatus, the interactive risk index dashboard, whereineach of the plurality of interactive risk index elements is displayed ina portion of the interactive risk index dashboard in accordance with arespective determined impact score and risk probability.